code: def preprocess_tratz_2011(folder_path):
      """
      Pre-process tratz_2011 dataset
  
      Args:
          folder_path (str): path to the unzipped tratz_2011 dataset
      """
      files = [
          "tratz2011_coarse_grained_random/train.tsv",
          "tratz2011_coarse_grained_random/" "val.tsv",
      ]
      dicts = [tratz2011_train_labeled_dict, tratz2011_val_labeled_dict]
      # 1. get abs path
      if not os.path.isabs(folder_path):
          # handle case using default value\relative paths
          folder_path = os.path.join(
              os.path.dirname(__file__), folder_path
          )
      # 2. add the location of the train file in the folder
      for file, dic in zip(files, dicts):
          file_full_path = os.path.join(folder_path, file)
          read_from_tratz_2011(file_full_path, dic)
  
file_path: nlp-architect/examples/np_semantic_segmentation/preprocess_tratz2011.py
imports: from __future__ import unicode_literals, print_function, division, absolute_import
  import argparse
  import csv
  import os
  from .data import absolute_path
  from nlp_architect.utils.io import validate_existing_directory
label_desc: Use image node_id 4 for calling a global function with example usage: preprocess_tratz_2011(folder_path) without return types